Triple
T19674586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Lucía metro station |
E472419
|
entity |
| Predicate | hasStationCode |
P1289
|
FINISHED |
| Object | SLU |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: SLU | Statement: [Santa Lucía metro station, hasStationCode, SLU]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SLU Context triple: [Santa Lucía metro station, hasStationCode, SLU]
-
A.
SLU
chosen
SLU is the station code for Santa Lucía, a stop on the Santiago Metro system in Chile.
-
B.
SLU
SLU is a Swedish university specializing in agricultural, environmental, and life sciences research and education.
-
C.
SLU
SLU is the IATA airport code for George F. L. Charles Airport, a regional airport serving Castries in Saint Lucia.
-
D.
SLU
SLU is a private Catholic university in Baguio City, Philippines, known for its comprehensive academic programs and significant role in higher education in Northern Luzon.
-
E.
SLU School of Law
SLU School of Law is the law school of Saint Louis University, known for its strong programs in health law, public interest, and practical legal training.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641bb2b7c8190b2badf12ce2caa52 |
completed | April 20, 2026, 3:09 p.m. |
Created at: April 10, 2026, 1:45 p.m.